feat: improve data training for models up to 7B parameters (#3085)

* feat: improve data training for models up to 7B parameters.

* docs: training considerations for small models to the documentation

docs/en/concepts/training.mdx

@@ -6,10 +6,10 @@ icon: dumbbell
 
 ## Overview
 
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
 
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
 This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
 ### Training Your Crew Using the CLI
@@ -42,8 +42,8 @@ filename = "your_model.pkl"
 
 try:
     YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
+      n_iterations=n_iterations,
+      inputs=inputs,
       filename=filename
     )
 
@@ -64,4 +64,68 @@ Once the training is complete, your agents will be equipped with enhanced capabi
 Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
 
 Happy training with CrewAI! 🚀
-  
+
+## Small Language Model Considerations
+
+<Warning>
+  When using smaller language models (≤7B parameters) for training data evaluation, be aware that they may face challenges with generating structured outputs and following complex instructions.
+</Warning>
+
+### Limitations of Small Models in Training Evaluation
+
+<CardGroup cols={2}>
+  <Card title="JSON Output Accuracy" icon="triangle-exclamation">
+    Smaller models often struggle with producing valid JSON responses needed for structured training evaluations, leading to parsing errors and incomplete data.
+  </Card>
+  <Card title="Evaluation Quality" icon="chart-line">
+    Models under 7B parameters may provide less nuanced evaluations with limited reasoning depth compared to larger models.
+  </Card>
+  <Card title="Instruction Following" icon="list-check">
+    Complex training evaluation criteria may not be fully followed or considered by smaller models.
+  </Card>
+  <Card title="Consistency" icon="rotate">
+    Evaluations across multiple training iterations may lack consistency with smaller models.
+  </Card>
+</CardGroup>
+
+### Recommendations for Training
+
+<Tabs>
+  <Tab title="Best Practice">
+    For optimal training quality and reliable evaluations, we strongly recommend using models with at least 7B parameters or larger:
+
+    ```python
+    from crewai import Agent, Crew, Task, LLM
+
+    # Recommended minimum for training evaluation
+    llm = LLM(model="mistral/open-mistral-7b")
+
+    # Better options for reliable training evaluation
+    llm = LLM(model="anthropic/claude-3-sonnet-20240229-v1:0")
+    llm = LLM(model="gpt-4o")
+
+    # Use this LLM with your agents
+    agent = Agent(
+        role="Training Evaluator",
+        goal="Provide accurate training feedback",
+        llm=llm
+    )
+    ```
+
+    <Tip>
+      More powerful models provide higher quality feedback with better reasoning, leading to more effective training iterations.
+    </Tip>
+  </Tab>
+  <Tab title="Small Model Usage">
+    If you must use smaller models for training evaluation, be aware of these constraints:
+
+    ```python
+    # Using a smaller model (expect some limitations)
+    llm = LLM(model="huggingface/microsoft/Phi-3-mini-4k-instruct")
+    ```
+
+    <Warning>
+      While CrewAI includes optimizations for small models, expect less reliable and less nuanced evaluation results that may require more human intervention during training.
+    </Warning>
+  </Tab>
+</Tabs>